Typical computer vision systems use point extraction to identify features in a scene. For example, typical techniques for identifying and extracting point features are Scale-invariant Feature Transform (SIFT), Speeded Up Robust Features (SURF), and Harris matrix. For example, typical techniques identify corners by detecting a change in gradient in the vertical and horizontal directions of the scene. In motion estimation systems, the identified corners or point features are correlated to point features in a prior scene and the difference in location is used to determine motion.
However, point extraction techniques are limited by perspective distortion. Perspective distortion refers to a change in the angle at which the scene is viewed. As the change in angle at which the image sensor views the scene increases, typical point extraction techniques become less effective in correlating point features. In fact, some typical point extraction techniques are unable to correlate or match any point features after a 45 degree change in view angle.
For the reasons stated above, and for other reasons stated below which will become apparent to those skilled in the art upon reading and understanding the present specification, there is a need in the art for an improved system and method of identifying and correlating features in a scene.